1. Field of the Invention
This invention relates to a method for monitoring of rotating machines, especially wind power plants, which are provided with a plurality of sensors for detection of physical parameters.
2. Description of Related Art
Vibration-diagnostic condition monitoring has been established for monitoring of mechanical components of rotating machines. Here, the vibrations of the individual components are continuously recorded as a quantity which describes the condition. The permanently measured data can be sent via online systems to a service center. This makes it possible to monitor a plurality of plants regardless of their locations. If conclusions about incipient component damage can be drawn from the measured vibration quantities, the operator of the plant is notified. He can then react promptly, depending on the type and severity of the damage.
In the case of a wind power plant, for example, due to the high complexity of the wind power plant as an overall construction, the analysis of vibration quantities is in part difficult. The plurality of effects and their mutual superposition make an unambiguous interpretation of the cause of the vibration with the conventional tools to some extent impossible. In addition, there is the problem that the rising number of plants to be monitored and the continued development of measurement systems toward seamless recording of states cause ever increasing amount of data.
Typically, in the condition monitoring of machines, a distinction is drawn between level 1, level 2, and level 3 operations, level 1 comprising the monitoring of measured values, for example, measured vibration values using predetermined threshold values, level 2 comprising a diagnosis, typically by an expert in a monitoring center, to answer the question “What damage is there?” and level 3 comprising an analysis of problems and fault causes.
A method which is often used in the condition monitoring of machines is called failure modes and effects analysis (FMEA), defined as a certain type of systematic procedure in the analysis of a system to determine possible failure modes, their causes and their effects on the system behavior. One example of the use of a FMEA method in the monitoring of a wind power plant (WEA) is described in German Patent Application DE 10 2008 006 370 A1 and corresponding U.S. Pat. No. 7,865,333 B2 for certain components of the wind power plant, certain characteristics being determined which arise as the product of certain index quantities, such as the frequency of the occurrence of a certain fault and the severity of the effect of this fault. These characteristics among others can be used in the evaluation of the diagnosis diagram, the current machine state and the risk of failure.
Furthermore, in the condition monitoring of machines statistical methods can be used in order to remove redundant information from the often very extensive measurement data sets by forming data clusters. One example of this is data processing using self-organizing maps (SOM); this was proposed for the first time by T. Kohonen in 1982.
International Patent Application Publication WO 2010/011918 A2 and corresponding U.S. Pat. No. 8,301,406 B2 describes level 1 monitoring of machines by the formation of SOM by means of tracking the time characteristic of the so-called quantization error of the SOM which has been formed. Furthermore, it is also mentioned that SOMs can also be used in the determination of the type of fault. U.S. Pat. No. 8,332,337 B2 also describes the use of SOMs in machine monitoring.
U.S. Pat. No. 7,778,947 B2 describes the monitoring of an air conditioning system by means of SOMs. U.S. Pat. No. 8,116,967 B2 describes the control of an internal combustion engine using SOMs. European Patent Application EP 0 845 720 A1 and corresponding U.S. Pat. No. 6,321,216 B1 and German Patent Application DE 197 34 947 A1 and corresponding U.S. Pat. No. 6,314,413 B1 describe the use of SOMs in the control of power plants, and it is also mentioned in European Patent Application EP 0 845 720 A1 and corresponding U.S. Pat. No. 6,321,216 B1 that the type of faults which occur in power plant operation can be determined from the evaluation of the patterns which arise in the SOMs.
European Patent Application EP 2 472 440 A1 describes the use of SOMs in the condition monitoring of production facilities, several different SOMs being proposed for different time bases and different space groups of the data which are delivered by the sensors; the different space groups can relate for example, to different plant parts.
In the article “Condition Monitoring: Adaptive Online Diagnosis Tool Optimizes Industrial Plants”, by C. W. Frey et al., in “Intelligent Production”, edition 2009 May, pages 38 to 39 the use of SOMs for industrial plants is described, plant monitoring by tracking of the quantization error using a so-called U-matrix representation for state classification being mentioned.
The article “A Process Monitoring System Based on the Kohonen Self-Organizing Maps” by S.-L. Jaemsa-Jounela et al., in “Control Engineering Practice” 11 (2003), pages 83 to 92 describes the use of SOMS in conjunction with heuristic rules for condition monitoring of plants, especially a copper smelting plant.
The article “Gear Box Condition Monitoring Using Self-Organizing Feature Maps” by G. Liao et al., in “Proceedings of the Institution of Mechanical Engineers”, Part. C: Journal of Mechanical Engineering Science 2004, 218, pages 119 to 129, describes monitoring of a gear box by means of the visualization of the U-matrix of SOMs.
The article “Modified Self-Organizing Map for Automated Novelty Detection Applied to Vibration Signal Monitoring” by M. L. D. Wong et al., in “Mechanical Systems and Signal Processing” 20, 2006, pages 593 to 610, describes the monitoring of bearings by means of SOMs.
European Patent Application EP 1 640 894 A1 and corresponding U.S. Pat. No. 7,464,063 B2 and European Patent Application EP 1 777 601 A1 which corresponds to U.S. Pat. No. 7,743,005 B2 and the article “Real Time Classification Algorithm for Recognition of Machine Operating Modes and by Use of Self Organizing Maps” by G. Vachkov et al., in “Turk. J. Elec. Engin.” 12, 2004, pages 27 to 42, describe the use of SOMs in the detection of the instantaneous operating state, for each known operating state its own SOM being set up.
International Patent Application Publication WO 2011/143531 A1 and the article “Wind Turbine Performance Assessment Easy—Multi Machine Modelling Approach” E. Lapira et al., in “Renewable Energy” 45, 2012, pages 86 to 95 describe the use of SOMs for condition monitoring in wind power plants, the quantization errors being evaluated in order to detect critical operating states.